
To learn task-specific representations in a self-supervised manner, we take inspiration from [1]} and employ a temporal cycle consistency loss. However, instead of directly using images, we propose a novel object-centric graph representation, which allows us to learn an embedding space that not only captures task-specific features, but depends solely on the spatial configuration of objects and their interactions. We here detail each component of our approach to representation learning.
